A passionate Data Science enthusiast who thrives on solving business problems using data-driven insights and cutting-edge technologies.
July 2024 – Present
August 2023 - August 2024
Led 15+ labs for IDS 270, teaching data analysis using Excel, Python, and statistical tools, increasing attendance by 20%. Facilitated discussions for 150+ students, enhancing critical thinking through engaging PowerPoint sessions. Graded 250+ assignments, providing detailed feedback that improved comprehension by 25%. Automated grade tracking and crafted presentations, boosting student engagement by 30% and reducing administrative errors by 40%.
July 2021 - August 2023
Led data-driven initiatives for a Fortune 500 retailer, leveraging SQL, Hive, Excel, Tableau, and GCP to optimize advertising strategies, boosting ROI by 15% and improving last-mile delivery speed by 15%. Conducted detailed analysis of KPIs across 50+ advertising campaigns, optimizing customer targeting by 20% and generating $500K in annual revenue. Designed and maintained 10+ real-time Tableau dashboards and custom Excel reports, reducing delivery costs by 10% and saving $1M annually. Automated reporting processes, reducing report generation time by 40%. Led A/B test analysis, improving test accuracy by 30% and increasing customer engagement by 10%. Improved on-time delivery rates by 20%, raising customer satisfaction scores by 15%, while using Jira to increase team productivity by 25%. Presented key insights through 10+ presentations, accelerating campaign optimization by 30%, and reduced delivery costs by 10%.
March 2019 - April 2019
Reduced downtime by analyzing operational data from electrical equipment.
Master of Science in Business Analytics
August 2023 - December 2024 | GPA: 4.0/4.0
Bachelor of Technology in Electrical & Electronics Engineering
July 2017 - May 2021 | GPA: 3.7/4.0
Python
SQL
C++
Google Cloud Platform
Tableau
Power BI
TensorFlow
Developed a machine learning model using Logistic Regression, K-Nearest Neighbors (KNN), and Linear SVC to classify toxic comments, significantly improving online moderation and effectively reducing harmful content visibility by a large margin. Applied advanced natural language processing (NLP) techniques to categorize comments, further improving moderation accuracy. Achieved 90% accuracy with Logistic Regression and 86% with KNN, enhancing safety in online spaces.
Created a sentiment analysis model using Random Forest, Naive Bayes, and XGBoost, achieving 92% accuracy with Random Forest. Utilized NLP techniques such as TF-IDF, Word2Vec, and sentiment analysis tools, enabling businesses to extract deeper, actionable insights from customer reviews, significantly enhancing product design, customer satisfaction, and long-term brand loyalty.
Expanded team size and student participation by organizing workshops, securing sponsorships, and increasing club revenue by 20%.
Represented the student body at outreach events, contributing to university initiatives.